| $device | $pin |
ESP32 Webhook
|
AI Agent
|
Telegram Voice Alert
Google Sheets
ThingSpeak
The AI Smart Autonomous Fire Detection Drone is an intelligent UAV system that detects fire hazards using ESP32, sensors, AI prediction logic, IoT cloud monitoring, n8n automation and Telegram voice alerts.
Drone Sensors
|
|
ESP32
|
WiFi / HTTP / MQTT
|
Cloud Platform
|
+----+-------+
| |
ThingSpeak n8n
Dashboard Automation
|
Telegram Voice Alert
Google Sheets Storage
Flame Sensor OUT -> ESP32 GPIO27 MQ2 Analog -> ESP32 GPIO34 DHT22 DATA -> ESP32 GPIO4 GPS TX -> ESP32 GPIO16 GPS RX -> ESP32 GPIO17
Sensors collect temperature, smoke, flame and location data. ESP32 processes the data and sends it to cloud services. AI logic calculates fire risk percentage. If danger is detected, n8n triggers Telegram voice alerts.
Fire Risk = Temperature Weight + Smoke Level + Flame Detection If Risk > 70% Status = HIGH FIRE ALERT
Read Sensors Connect WiFi Calculate Fire Risk Send Data to n8n Webhook Upload to Cloud
ESP32 Webhook
|
AI Agent Analysis
|
IF Fire Detected
|
Telegram Voice Alert
|
Google Sheets Logging
Store: Temperature, Smoke Level, Fire Risk, GPS Location and Time.
Power Usage = Motor Load + Flight Time + Sensor Consumption Predict Remaining Battery and Return Drone if required.
Build an autonomous farming rover using ESP32, GPS navigation, sensors, AI prediction logic, n8n automation, Telegram voice alerts, Google Sheets, and ThingSpeak cloud dashboard.
GPS TX -> ESP32 GPIO16 GPS RX -> ESP32 GPIO17 Soil Sensor -> GPIO34 DHT22 -> GPIO4 Ultrasonic Trigger -> GPIO5 Ultrasonic Echo -> GPIO18 Motor Driver: IN1 -> GPIO25 IN2 -> GPIO26 IN3 -> GPIO27 IN4 -> GPIO14
Start | Initialize ESP32 | Connect WiFi | Read Sensors | GPS Navigation | Obstacle Detected? | Yes -> Avoid Obstacle | No -> Continue Movement | Send Cloud Data | AI Analysis | Telegram Alert
Read GPS coordinates Read soil moisture Read temperature Check obstacle distance Control motors Send data to cloud
Power = Voltage x Current Future Consumption = Current Power + Motor Load + Distance Factor + Terrain Factor
Fields: Temperature, Humidity, Soil Moisture, Battery, Latitude and Longitude.
ESP32 | Webhook | AI Processing | Telegram Voice Alert | Google Sheets Logging
n8n automatically stores farming sensor data with time, location and battery information.
AI powered autonomous farming vehicle with IoT dashboard, cloud monitoring, automation and voice notification system.
HTML; echo "
ESP32 + AI Agent + n8n + Telegram Voice Alerts + Google Sheets + ThingSpeak
This project automatically evaluates student answer sheets using AI, stores marks in Google Sheets, updates ThingSpeak dashboards, and sends Telegram voice notifications.
Student Answer Sheet
|
V
ESP32-CAM
|
V
WiFi
|
V
n8n Server
|
-----------------
| | |
OCR AI Database
| |
---------
|
V
Google Sheets
|
V
ThingSpeak
|
V
Telegram Voice Alert
| Component | Quantity |
|---|---|
| ESP32-CAM | 1 |
| ESP32 Development Board | 1 |
| OV2640 Camera | 1 |
| WiFi Router | 1 |
| USB TTL Converter | 1 |
| Breadboard | 1 |
| Jumper Wires | As Required |
| Power Supply | 5V 2A |
START | Initialize ESP32 | Connect WiFi | Capture Image | Upload to n8n | OCR Processing | AI Evaluation | Generate Marks | Store Results | Send Telegram Alert | END
#include <WiFi.h>
#include <HTTPClient.h>
const char* ssid = "YOUR_WIFI";
const char* password = "YOUR_PASSWORD";
void setup()
{
Serial.begin(115200);
WiFi.begin(ssid,password);
while(WiFi.status()!=WL_CONNECTED)
{
delay(500);
}
}
void loop()
{
HTTPClient http;
http.begin(
"https://yourserver.com/webhook/exam");
http.addHeader(
"Content-Type",
"application/json");
String data =
"{\"student\":\"101\"}";
http.POST(data);
http.end();
delay(30000);
}
Webhook | OCR | AI Evaluation | Score Calculation | --------------------- | | | Google ThingSpeak Telegram Sheets Voice
| Student ID | Subject | Marks | Similarity | Feedback |
|---|---|---|---|---|
| 101 | Maths | 85 | 90% | Good Performance |
| Field | Description |
|---|---|
| Field1 | Marks |
| Field2 | Similarity |
| Field3 | Evaluation Time |
| Field4 | AI Confidence |
| Field5 | Power Consumption |
AI Result
|
Text Message
|
Google TTS
|
MP3 Audio
|
Telegram Send Audio
Example Voice: "Student 101 scored 85 marks. Performance is excellent."
Formula:
Power = Voltage x Current Example: 5V x 0.24A Power = 1.2 Watts
CREATE TABLE students( id INT, name VARCHAR(50), subject VARCHAR(30), marks FLOAT, similarity FLOAT, feedback TEXT, timestamp DATETIME );
ESP32-CAM
|
Internet
|
Cloud Server
|
n8n Automation
|
OpenAI / Gemini
|
Google Sheets
|
ThingSpeak
|
Telegram Alerts
SunCharge is an intelligent EV charging station that uses solar energy as its primary power source, combined with battery storage and secure digital access control. The system provides sustainable, cost-effective, and secure charging for electric vehicles while reducing dependence on the power grid.
Solar Panels
│
▼
MPPT Charge Controller
│
▼
Battery Storage System
│
▼
Smart Power Management Unit
│
├── EV Charging Port
├── IoT Monitoring Module
├── RFID/User Authentication
└── Safety & Security System
| Parameter | Benefit |
|---|---|
| Energy Cost | Reduced through solar generation |
| Sustainability | Lower carbon emissions |
| Reliability | Battery backup support |
| Security | Controlled user access |
| Monitoring | Real-time analytics |
| Scalability | Supports future expansion |
SunCharge integrates solar power, battery storage, IoT monitoring, RFID authentication, and cybersecurity into a single intelligent platform. It offers a sustainable, secure, and scalable solution for the future of EV charging infrastructure.
This project is designed to improve road safety by integrating accident detection, alcohol sensing, GPS tracking, and GSM communication using ESP32. The system can prevent drunk driving and automatically send emergency alerts with vehicle location during accidents.
+----------------+
| MQ-3 Sensor |
+-------+--------+
|
v
+--------------------------------+
| ESP32 |
| |
| - Alcohol Monitoring |
| - Accident Detection |
| - GPS Data Processing |
| - GSM Communication |
+-----+-------------+------------+
| |
v v
+-----------+ +----------+
| GPS Module| | GSM SIM |
| NEO-6M | |800L/SIM900|
+-----------+ +----------+
|
v
Location Coordinates
|
v
+------------+
| Relay Motor|
| Control |
+------------+
|
v
+------------+
| Buzzer/LCD |
+------------+
| Component | Quantity |
|---|---|
| ESP32 Development Board | 1 |
| MQ-3 Alcohol Sensor | 1 |
| GPS Module (NEO-6M) | 1 |
| GSM Module (SIM800L/SIM900A) | 1 |
| Vibration Sensor / MPU6050 | 1 |
| Relay Module | 1 |
| DC Motor | 1 |
| Buzzer | 1 |
| LCD 16x2 I2C | 1 |
| Power Supply | 1 |
Sample Alert Message:
ALERT! Vehicle Accident Detected. Location: https://maps.google.com/?q=17.3850,78.4867 Need Immediate Assistance.
| Module | ESP32 Pin |
|---|---|
| MQ3 AO | GPIO34 |
| Vibration Sensor | GPIO27 |
| GPS TX | GPIO16 |
| GPS RX | GPIO17 |
| GSM TX | GPIO26 |
| GSM RX | GPIO25 |
| Relay | GPIO18 |
| Buzzer | GPIO19 |
| LCD SDA | GPIO21 |
| LCD SCL | GPIO22 |
START
Initialize ESP32
Initialize GPS
Initialize GSM
Initialize MQ3
LOOP
Read Alcohol Sensor
IF Alcohol > Threshold
Stop Vehicle
Activate Alarm
ENDIF
Read Accident Sensor
IF Accident Detected
Read GPS Location
Send SMS via GSM
Activate Buzzer
ENDIF
Repeat
The system detects alcohol before vehicle operation, prevents ignition when alcohol is present, and automatically sends GPS-based accident alerts through GSM during emergencies, improving vehicle safety and reducing response time.
